Short-Term and Long-Term Changes of Nasal Soft Tissue after Rapid Maxillary Expansion (RME) with Tooth-Borne and Bone-Borne Devices. A CBCT Retrospective Study.
Bibliographic record
Abstract
The objective of the study was to assess the changes in nasal soft tissues after RME was performed with tooth-borne (TB) and bone-borne (BB) appliances. Methods. This study included 40 subjects with a diagnosis of posterior cross-bite who received tooth-borne RME (TB, average age: 11.75 ± 1.13 years) or bone-borne RME (BB, average age: 12.68 ± 1.31 years). Cone-beam computed tomography (CBCT) was taken before treatment (T0), after a 6-month retention period (T1), and one year after retention (T2). Specific linear measurements of the skeletal components and of the soft-tissue region of the nose were performed. All data were statistically analyzed. Results. Concerning skeletal measurements, the BB group showed a greater skeletal expansion of the anterior and posterior region of the nose compared to the TB group (p < 0.05) immediately after RME. Both TB and BB RME induce a small increment (>1 mm) of the alar base and alar width, without significant differences between the two expansion methods (p > 0.05). A high correlation was found between skeletal and soft-tissue expansion in the TB group; instead, a weaker correlation was found in the BB group. Conclusion. A similar slight increment of the alar width and alar base width was found in both TB and BB groups. However, the clinical relevance of these differences, in terms of facial appearance, remains questionable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".